Has the Digital Economy Changed the Urban Network Structure in China?—Based on the Analysis of China’s Top 500 New Economy Enterprises in 2020

The rapid development of the new generation of information technology makes digital enterprises and the digital economy important forces in promoting the sustainable growth of the world economy. Under the influence of the digital economy, the original urban network may undergo drastic changes. There have been studies that have arrived at conflicting conclusions. This paper primarily illustrates whether or not the digital economy has changed the urban network structure. China's digital economy is developing rapidly, becoming a new engine for the high-quality development of the Chinese economy. Therefore, this paper demonstrates the impact of China's digital economy on the urban network structure by using data from China's Top 500 New Economy Enterprises in 2020 and the headquarter–subsidiary ownership method. The results show that 1) China's urban network has changed significantly. Compared with APS enterprises and listed companies, the urban network of the digital economy has become more polarized, and Beijing has become the absolute control center. 2) Chinese cities have been reshuffled in the era of the digital economy. Beijing, Hangzhou, and Chengdu, with their industrial foundations in the digital economy, have performed better within the network. Simultaneously, some heavily industrialized cities, such as Wuhan, Shenyang, and Chongqing, have been declining due to the difficulties associated with transformation. 3) Although the digital economy has reshaped China's urban network structure to a certain extent, the original urban pattern still plays a dominant role in the new system. The network spatial pattern of dense east and sparse west still exists, and provincial capitals and subprovincial cities still play a more significant role in the network than ordinary cities. 4) Network diffusion is typically a hierarchical diffusion between core nodes. Geographical proximity has a low constraint on network diffusion, and subsidiaries expand outward through hierarchical diffusion.

A network-based method for predicting disease-associated enhancers

Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

Network diffusion model predicts neurodegeneration in limb-onset amyotrophic lateral sclerosis

The trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. We measured degeneration in limb-onset ALS patients (n=14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n=12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.

Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies

Network diffusion models are a common and powerful way to study the propagation of information through a complex system, they and offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with impaired information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous internalizing psychopathology (IP) cohort. This research provides preliminary evidence of a transdiagnostic deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, that may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identity diffusion disrupted subnetworks and potential targets for neuromodulatory intervention based on previously well-characterized methodology.